@ARTICLE{Budziński_Wiktor_Accounting_2021,
 author={Budziński, Wiktor and Czajkowski, Mikołaj},
 number={No 1},
 journal={Central European Journal of Economic Modelling and Econometrics},
 pages={1-24},
 howpublished={online},
 year={2021},
 publisher={Oddział PAN w Łodzi},
 abstract={There are reasons researchers may be interested in accounting for spatial heterogeneity of preferences, including avoiding model misspecification and the resulting bias, and deriving spatial maps of willingness-to-pay (WTP), which are relevant for policy-making and environmental management. We employ a Monte Carlo simulation of three econometric approaches to account for spatial preference heterogeneity in discrete choice models. The first is based on the analysis of individual-specific estimates of the mixed logit model. The second extends this model to explicitly account for spatial autocorrelation of random parameters, instead of simply conditioning individual-specific estimates on population-level distributions and individuals’ choices. The third is the geographically weighted multinomial logit model, which incorporates spatial dimensions using geographical weights to estimate location-specific choice models. We analyze the performance of these methods in recovering population-, region- and individual-level preference parameter estimates and implied WTP in the case of spatial preference heterogeneity. We find that, although ignoring spatial preference heterogeneity did not significantly bias population-level results of the simple mixed logit model, neither individual-specific estimates nor the geographically weighted multinomial logit model was able to reliably recover the true region- and individual-specific parameters. We show that the spatial mixed logit proposed in this study is promising and outline possibilities for future development.},
 type={Article},
 title={Accounting for Spatial Heterogeneity of Preferences in Discrete Choice Models},
 URL={http://www.journals.pan.pl/Content/119745/1_1_2021.pdf},
 doi={10.24425/cejeme.2021.137353},
 keywords={discrete choice experiment, discrete choice models, individual-, region- and population-level parameter estimates, spatial preference heterogeneity},
}